Optimizing a gamified design through reinforcement learning

Título:

Optimizing a gamified design through reinforcement learning : a case study in stack overflow

Autor:

Martin, Jonathan

Colaboradores:

; Torres, Diego; Fernández, Alejandro

Temas:

OPTIMIZACIÓN

En:

Jornadas de Cloud Computing, Big Data & Emerging Topics (9na : 2021 : La Plata, Argentina)

Resumen:

Gamification can be used to foster participation in knowledge sharing communities. While designing and assessing the potential impact of a gamification design in such a context, it is important to avoid work disruption and negative side effects. A gamification optimization approach implemented with deep reinforcement learning based on play-testing approaches helps prevent possible disruptive configuration and has the capability to adapt to different communities or gamification targets. In this research, a case of study for this approach is presented running over the Stack Overflow Q&A community. The approach detects the best configuration for a Contribution, Reinforcement, and Dissemination (CRD) gamification strategy using Stack Overflow historical data in a year. The results show that the approach funds proper gamification strategy configurations. Moreover, those configurations are robust enough to be applied along the time unseen periods.

URL/DOI:

http://dx.doi.org/10.1007/978-3-030-84825-5_7

Palabras clave:

gamificación

Medio:

Soporte electrónico

Tipo de documento:

Artículo

Descripción física:

1 archivo (358,4 kB)

Idioma:

Inglés

Publicación:

, 2021

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